73 lines
2.8 KiB
Markdown
73 lines
2.8 KiB
Markdown
# Podcast Maker Journey - Developers
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Use this journey to integrate Podcast Maker into repeatable, testable pipelines for scripted audio generation and distribution.
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## Overview
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### Entry Conditions
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- **Inputs:** API credentials, topic payload schema, content constraints, output destination.
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- **Skill level:** Intermediate to advanced (API and workflow automation).
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- **Expected time:** 60-120 minutes for first implementation.
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### Success Target
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Automate one full podcast generation path from prompt to exported artifact with predictable quality.
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## Setup
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### Recommended Defaults
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- **Duration:** 10-20 minutes (configurable per template)
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- **Speakers:** 1-2 synthetic speakers
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- **Voice style:** Neutral/professional with stable pacing
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- **Research provider:** Perplexity (structured fact gathering for scripted outputs)
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### Pre-Production Checklist
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1. Define request schema for analysis/research/script/render/export stages.
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2. Store provider credentials via environment variables.
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3. Configure retry/error policy for external research and render calls.
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4. Add logging for prompt versions and output hashes.
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## Production
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### Podcast Maker Workflow
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1. **Analysis**
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- Validate input payload and enforce required fields.
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- Derive episode objective and section plan programmatically.
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2. **Research**
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- Fetch source context with provider abstraction.
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- Normalize citations and drop low-confidence results.
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3. **Script**
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- Generate structured script JSON (intro/segments/outro/CTA).
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- Run lint-style checks for length and forbidden terms.
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4. **Render**
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- Render audio using configured speaker profile.
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- Execute post-render QA hooks (duration, loudness, clipping checks).
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5. **Export**
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- Persist artifact + metadata to storage.
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- Trigger downstream publish/webhook integration.
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## Optimization
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### Success Criteria
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- End-to-end pipeline completes without manual intervention.
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- Output passes automated quality checks.
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- Metadata includes provenance for research and prompt version.
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- Failure paths are observable with actionable logs.
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### Checkpoints
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- **Before render:** Unit/integration checks pass for script payload.
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- **After render:** Verify duration bounds and transcript alignment.
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- **After publish:** Monitor error rate, latency, and output quality metrics.
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## Troubleshooting
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### Common Issues and Fixes
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- **Provider timeouts:** Add retries with exponential backoff and fallback provider.
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- **Inconsistent scripts:** Pin model settings and enforce schema validation.
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- **Audio quality failures:** Add deterministic render settings and QA thresholds.
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- **Broken exports:** Validate storage credentials and file naming conventions.
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- **Debug difficulty:** Log stage-level inputs/outputs with correlation IDs.
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---
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Next step: integrate this into **[Advanced Usage](advanced-usage.md)** automation patterns.
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